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Anaconda installation

   Anaconda is a software package built to facilitate the use of python, which contains more than 250 commonly used toolkits, multi-version python interpreters and powerful virtual environment management tools, so Anaconda is named python family bucket. Anaconda makes it easier to install, run, and upgrade the environment, so installation is recommended.

Installation steps:

Download the installation package https://www.anaconda.com/distribution/#download-section to run the installation package choose the installation path: usually choose the default path, be sure to check Add Anaconda to the system PATH environment variable (add Anaconda to the environment variable), and wait for the installation to be completed to verify the success of the installation: shortcut key win+R, open cmd, type conda, enter enter, if various relevant information appears, the installation is successful.

PyCharm installation

   PyCharm is a powerful Python IDE, with debugging, syntax highlighting, Project management, code jump, intelligent prompts, version control and other functions.

Installation steps:

Download the installation package https://www.jetbrains.com/pycharm/ on the official website, which is divided into professional version (fee) and community version (free). Run the installation package. Select the path, check Add launchers dir to the PATH, check .py, and wait for the installation to complete.

CUDA and CuDNN installation (optional)

Check if there is a suitable GPU, and if so, install CUDA and CuDNN. Only N card supports cuda. You can check the supported cuda versions by doing the following: NVIDIA Control Panel → system Information → component → 3D Settings / NVCUDA.DLL enter the PyTorch official website https://pytorch.org/, click GetStarted to see how many CUDA versions are supported. Go to the CUDA official website https://developer.nvidia.com/cuda-toolkit-archive, select the corresponding version of CUDA, select the appropriate operating system, and select local for Installer Type. Click to download the first file. Run the installation package, and you don't have to create a shortcut when the installation is complete. Verify that CUDA is installed successfully: enter the bin folder of the installation path, copy the path, switch the command line to that path (such as cd C:\ Program Files\ NVIDIA GPU Computing Toolkit\ CUDA\ v10.1\ bin), then execute nvcc-V, enter, and if the relevant version information appears, the installation is correct. Go to the cuDNN official website https://developer.nvidia.com/rdp/cudnn-download, register and log in to the account, and select the appropriate version to download. Extract the installation package, copy the three folders inside to the CUDA installation path (such as C:\ Program Files\ NVIDIA GPU Computing Toolkit\ CUDA\ v10.1), and the cuDNN installation is complete. Verify that cuDNN is installed successfully: change the command line to the extras\ demo_suite folder in the installation path, execute bandwidthTest.exe, enter, and Result = PASS indicates that the installation is successful. Continue to execute deviceQuery.exe, enter, and display the GPU model number, Result = PASS, indicating that both CUDA and cuDNN have been installed successfully.

Change the pip source to the domestic image

When    installs the python toolkit using pip, it is recommended to switch domestic images because the default source network speed is very slow.

Default source

Https://pypi.org/

Domestic mirror image

Aliyun https://mirrors.aliyun.com/pypi/simple/

Https://pypi.mirrors.ustc.edu.cn/simple/ of University of Science and Technology of China

Douban https://pypi.douban.com/simple/

Https://pypi.tuna.tsinghua.edu.cn/simple/ of Tsinghua University

Https://pypi.hustunique.com/ of Huazhong University of Science and Technology

Temporary use

The installation command is followed by-I url, such as downloading pandas with Tsinghua image

Pip install pandas-I https://pypi.tuna.tsinghua.edu.cn/simple

Permanent modification

Modify the% HOMEPATH%\ pip\ pip.ini file

% HOMEPATH% is usually C:\ Users\ xx

Usually, you need to create a new folder pip, and then the new file pip.ini,ini file can be opened and edited with notepad. Enter the following in the pip.ini file (take Douban image as an example):

[global]

Index-url = https://pypi.douban.com/simple

[install]

Trusted-host = pypi.douban.com

PyTorch installation

1. Download whl file (optional)

Go to the PyTorch official website https://pytorch.org/, click GetStarted, select the corresponding version, and package select pip. You can see the latest version numbers of torch and torchvision below, as well as a URL https://download.pytorch.org/whl/torch_stable.html. Copy the URL and enter, you can see the installation files of each version of torch. In this way, it is faster than directly executing the installation command. File naming is regular, such as:

Cu101/torch-1.4.0-cp37-cp37m-win_amd64.whl

Cu101/torchvision-0.5.0-cp37-cp37m-win_amd64.whl

Cu is the cuda version, torch is the torch version, torchvision is the torchvision version, cp is the python version, and the last face represents the windows 64-bit.

You can use the shortcut key ctrl+F to search for the latest version of torch and torchvision files (such as search cu101/torch-1.4.0), and then select the appropriate python version and platform to download pytorch and torchvision whl files. To keep the python version consistent with the system, you can enter python on the command line to view the version of python in the system.

two。 Create a new project with PyCharm

Create New Project → Pure Python → named → Create

Create a new script: File → New → Python file → named → enter

Enter the following code in the script → right-click → Run 'project name' → failed to find torch because we do not have PyTorch installed in the current environment.

Import torchprint ("hello pytorch {}" .format (torch.__version__)) print (torch.cuda.is_available ())

3. Create a python virtual environment

Click Terminal → below to enter conda create-n virtual environment name python= version number (such as conda create-n pytorch_gpu python=3.7) → enter → to wait for completion

Enter the virtual environment: enter the conda activate virtual environment name →

4. Installation

Enter the directory where the whl file is located: enter the directory where the cd whl file is located, enter →

Installation: enter pip install torch → and press tab to automatically complete → enter → waiting for successful installation

Enter pip install torchvision → and press tab to automatically complete → enter → waiting for successful installation

Note: if the whl file is not downloaded in the first step, install it directly with the pip or conda command, which will be displayed after selecting the appropriate version on the PyTorch official website.

5. To associate the current project with the newly created virtual environment, select the python interpreter

File → Setting → Project: project name / Project Interpreter → setup button → Add → Conda Environment → Existing environment → interpreter Select anaconda installation path / envs/ virtual environment name / python.exe → OK → to initialize in a moment

6. Verification

Right-click to run and successfully output the PyTorch version. If you output True, prove that GPU is available.

Summary

This is the end of this article on the installation tutorial of the PyTorch development environment under Windows. For more information about the installation of the windows PyTorch environment, please search for previous articles or continue to browse the relevant articles below. I hope you will support it in the future!

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